Sentiment analysis refers to the processing and analysis of source materials to extract subjective information such as the attitudes or opinions of the authors of the source materials. Existing sentiment analysis techniques use only the source materials as the basis for determining sentiments and can often lead to errors.
Some existing techniques use a dictionary-based approach in which certain words are classified (e.g., labeled) to indicate positive or negative sentiments, and sentences including these words take on the same sentiment as the words. For example, the word “long” may be deemed to be positive in the dictionary. In practice, however, the word “long” may reflect different sentiments of the author depending on the topic discussed in the same text in which the word “long” is used. For instance, “the battery life is long” indicates a positive sentiment, but “the check-in line is long” indicates a negative sentiment. As another example, the same answer to different questions can indicate different sentiments. The answers in response to the questions of “What compliments do you have for our staff?” and “What could we have done to improve?” are both “Housekeeping.” In response to the first question, the answer indicates a positive sentiment; in response to the second question, however, the same answer indicates a negative sentiment. Existing sentiment analysis techniques often misidentify the sentiments in such cases.